Human detection within an image continues to gain more attention from academia and industry due to applications in video surveillance, content-based image/video retrieval, video annotation, human-computer interaction, and so forth. Legacy research on this topic has considered human detection from RGB images using learning techniques, which have involved Histogram of Oriented Gradients features, Deformable Part-based Model, and Convolutional Neural Networks. Differences between public datasets that include trained samples used for learning and more complicated real scenarios of images exist create challenges in recognition quality. Furthermore, full-body based human detection may be difficult in images with high density crowds were one body may occlude another.